Fix inceptionv3 (#1446)

nnvm/python/nnvm/frontend/mxnet.py

@@ -56,6 +56,8 @@ def _pooling(inputs, attrs):
         new_attrs['strides'] = attrs.get('stride', (1, 1))
         new_attrs['padding'] = attrs.get('pad', (0, 0))
         new_attrs['ceil_mode'] = (attrs.get('pooling_convention', 'valid') == 'full')
+        if pool_type == 'avg':
+            new_attrs['count_include_pad'] = attrs.get('count_include_pad', True)
     return _get_nnvm_op(op_name)(*inputs, **new_attrs)
 
 def _batch_norm(inputs, attrs):

nnvm/tests/python/compiler/test_top_level2.py

@@ -10,6 +10,20 @@ from nnvm.testing.config import ctx_list
 
 
 def test_conv2d():
+    def run_test_conv2d(sym, dtype, dshape, kshape, oshape, shape_dict, padding):
+        for target, ctx in ctx_list():
+            graph, lib, _ = nnvm.compiler.build(sym, target, shape_dict)
+            m = graph_runtime.create(graph, lib, ctx)
+            data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
+            kernel = tvm.nd.array(np.random.uniform(size=kshape).astype(dtype))
+            bias = tvm.nd.array(np.random.uniform(size=kshape[0]).astype(dtype))
+            m.run(x=data, y_weight=kernel, y_bias=bias)
+            out = m.get_output(0, tvm.nd.empty(oshape, dtype))
+            c_np = topi.testing.conv2d_nchw_python(
+                data.asnumpy(), kernel.asnumpy(), 1, padding)
+            c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1)
+            np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
+
     x = sym.Variable("x")
     y = sym.conv2d(x, channels=10, kernel_size=(3,3),
                    name="y", padding=(1,1))
@@ -18,18 +32,17 @@ def test_conv2d():
     kshape = (10, 3, 3, 3)
     oshape = (1, 10, 18, 18)
     shape_dict = {"x": dshape}
-    for target, ctx in ctx_list():
-        graph, lib, _ = nnvm.compiler.build(y, target, shape_dict)
-        m = graph_runtime.create(graph, lib, ctx)
-        data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
-        kernel = tvm.nd.array(np.random.uniform(size=kshape).astype(dtype))
-        bias = tvm.nd.array(np.random.uniform(size=kshape[0]).astype(dtype))
-        m.run(x=data, y_weight=kernel, y_bias=bias)
-        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
-        c_np = topi.testing.conv2d_nchw_python(
-            data.asnumpy(), kernel.asnumpy(), 1, 1)
-        c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1)
-        np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
+    run_test_conv2d(y, dtype, dshape, kshape, oshape, shape_dict, (1,1))
+
+    x = sym.Variable("x")
+    y = sym.conv2d(x, channels=10, kernel_size=(1,3),
+                   name="y", padding=(0,1))
+    dtype = "float32"
+    dshape = (1, 3, 224, 224)
+    kshape = (10, 3, 1, 3)
+    oshape = (1, 10, 224, 224)
+    shape_dict = {"x": dshape}
+    run_test_conv2d(y, dtype, dshape, kshape, oshape, shape_dict, (0,1))
 
 
 def test_mixed_precision():

nnvm/tests/python/frontend/mxnet/test_forward.py

@@ -141,6 +141,14 @@ def test_forward_expand_dims():
     mx_sym = mx.sym.expand_dims(data, axis=1)
     verify_mxnet_frontend_impl(mx_sym, (2, 3, 4), (2, 1, 3, 4))
 
+def test_forward_pooling():
+    data = mx.sym.var('data')
+    mx_sym = mx.sym.Pooling(data, kernel=(3, 3), pad=(1, 1), pool_type='avg')
+    verify_mxnet_frontend_impl(mx_sym, (1, 20, 8, 8), (1, 20, 8, 8))
+
+    mx_sym = mx.sym.Pooling(data, kernel=(3, 3), pad=(1, 1), pool_type='max')
+    verify_mxnet_frontend_impl(mx_sym, (1, 20, 8, 8), (1, 20, 8, 8))
+
 if __name__ == '__main__':
     test_forward_mlp()
     test_forward_vgg()
@@ -154,3 +162,4 @@ if __name__ == '__main__':
     test_forward_split()
     test_forward_split_squeeze()
     test_forward_expand_dims()
+    test_forward_pooling()

topi/python/topi/testing/conv2d_nchw_python.py

-# pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals
+# pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals, too-many-branches
 """Convolution in python"""
 import numpy as np
 import scipy.signal
@@ -18,8 +18,8 @@ def conv2d_nchw_python(a_np, w_np, stride, padding):
     stride : int or a list/tuple of two ints
         Stride size, or [stride_height, stride_width]
 
-    padding : int or str
-        Padding size, or ['VALID', 'SAME']
+    padding : int or str or a list/tuple of two ints
+        Padding size, or ['VALID', 'SAME'], or [pad_height, pad_width]
 
     Returns
     -------
@@ -34,12 +34,11 @@ def conv2d_nchw_python(a_np, w_np, stride, padding):
         stride_h, stride_w = stride
     if isinstance(padding, int):
         pad_h = pad_w = padding * 2
-    elif padding == 'VALID':
-        pad_h = 0
-        pad_w = 0
-    else: # 'SAME'
-        pad_h = kernel_h - 1
-        pad_w = kernel_w - 1
+    elif isinstance(padding, (list, tuple)):
+        pad_h, pad_w = padding[0] * 2, padding[1] * 2
+    else:
+        pad_h = 0 if padding == 'VALID' else kernel_h - 1
+        pad_w = 0 if padding == 'VALID' else kernel_w - 1
     pad_top = int(np.ceil(float(pad_h) / 2))
     pad_bottom = pad_h - pad_top
     pad_left = int(np.ceil(float(pad_w) / 2))
@@ -53,9 +52,14 @@ def conv2d_nchw_python(a_np, w_np, stride, padding):
     for n in range(batch):
         for f in range(out_channel):
             for c in range(in_channel):
-                if pad_h > 0:
+                if pad_h > 0 or pad_w > 0:
                     apad = np.zeros((in_height + pad_h, in_width + pad_w))
-                    apad[pad_top:-pad_bottom, pad_left:-pad_right] = a_np[n, c]
+                    if pad_h == 0:
+                        apad[:, pad_left:-pad_right] = a_np[n, c]
+                    elif pad_w == 0:
+                        apad[pad_top:-pad_bottom, :] = a_np[n, c]
+                    else:
+                        apad[pad_top:-pad_bottom, pad_left:-pad_right] = a_np[n, c]
                 else:
                     apad = a_np[n, c]
                 out = scipy.signal.convolve2d(

topi/python/topi/x86/conv2d_avx_1x1.py

@@ -56,7 +56,7 @@ def _declaration_conv(data, kernel, stride, padding, layout, out_dtype):
     out_height = (in_height + 2 * HPAD - kernel_height) // HSTR + 1
     out_width = (in_width + 2 * WPAD - kernel_width) // WSTR + 1
 
-    DOPAD = (HPAD != 0 and WPAD != 0)
+    DOPAD = (HPAD != 0 or WPAD != 0)
     if DOPAD:
         data_pad = pad(data, (0, 0, HPAD, WPAD), name="data_pad")
     else:
@@ -95,7 +95,7 @@ def _schedule_conv(s, data, data_pad, data_vec, kernel, kernel_vec, conv_out, ou
     sch = _get_schedule(wkl)
 
     HPAD, WPAD = wkl.hpad, wkl.wpad
-    DOPAD = (HPAD != 0 and WPAD != 0)
+    DOPAD = (HPAD != 0 or WPAD != 0)
 
     A, W = data, kernel_vec
     A0, A1 = data_pad, data_vec
@@ -163,7 +163,7 @@ def _declaration_conv_NCHWc(wkl, sch, data, kernel):
     out_height = (wkl.height + 2 * HPAD - wkl.hkernel) // HSTR + 1
     out_width = (wkl.width + 2 * WPAD - wkl.wkernel) // WSTR + 1
 
-    DOPAD = (HPAD != 0 and WPAD != 0)
+    DOPAD = (HPAD != 0 or WPAD != 0)
     if DOPAD:
         data_pad = pad(data, (0, 0, HPAD, WPAD, 0), name="data_pad")
     else:

topi/python/topi/x86/conv2d_avx_common.py

@@ -58,7 +58,7 @@ def _declaration_conv(data, kernel, stride, padding, layout, out_dtype):
     out_width = (in_width + 2 * WPAD - kernel_width) // WSTR + 1
 
     # pack data
-    DOPAD = (HPAD != 0 and WPAD != 0)
+    DOPAD = (HPAD != 0 or WPAD != 0)
     if DOPAD:
         data_pad = pad(data, (0, 0, HPAD, WPAD), name="data_pad")
     else:
@@ -108,7 +108,7 @@ def _schedule_conv(s, data, data_pad, data_vec, kernel, kernel_vec, conv_out, ou
     sch = _get_schedule(wkl)
 
     HPAD, WPAD = wkl.hpad, wkl.wpad
-    DOPAD = (HPAD != 0 and WPAD != 0)
+    DOPAD = (HPAD != 0 or WPAD != 0)
 
     A, W = data, kernel_vec
     A0, A1 = data_pad, data_vec
@@ -181,7 +181,7 @@ def _declaration_conv_NCHWc(wkl, sch, data, kernel):
     out_width = (wkl.width + 2 * WPAD - wkl.wkernel) // WSTR + 1
 
     # pack data
-    DOPAD = (HPAD != 0 and WPAD != 0)
+    DOPAD = (HPAD != 0 or WPAD != 0)
     if DOPAD:
         data_pad = pad(data, (0, 0, HPAD, WPAD, 0), name="data_pad")
     else: